Overview

Dataset statistics

Number of variables39
Number of observations8180
Missing cells38710
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory312.0 B

Variable types

Numeric27
Categorical10
Boolean2

Warnings

razaoSocial has a high cardinality: 2725 distinct values High cardinality
nomeFantasia has a high cardinality: 2643 distinct values High cardinality
cnpjSemTraco has a high cardinality: 2789 distinct values High cardinality
primeiraCompra has a high cardinality: 1927 distinct values High cardinality
dataAprovadoEmComite has a high cardinality: 558 distinct values High cardinality
periodoBalanco has a high cardinality: 124 distinct values High cardinality
dataAprovadoNivelAnalista has a high cardinality: 7011 distinct values High cardinality
df_index is highly correlated with numero_solicitacaoHigh correlation
numero_solicitacao is highly correlated with df_indexHigh correlation
margemBrutaAcumulada is highly correlated with scorePontualidadeHigh correlation
titulosEmAberto is highly correlated with valorAprovadoHigh correlation
diferencaPercentualRisco is highly correlated with percentualRiscoHigh correlation
percentualRisco is highly correlated with diferencaPercentualRiscoHigh correlation
valorAprovado is highly correlated with titulosEmAbertoHigh correlation
ativoCirculante is highly correlated with passivoCirculante and 3 other fieldsHigh correlation
passivoCirculante is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
totalAtivo is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
endividamento is highly correlated with estoque and 3 other fieldsHigh correlation
duplicatasAReceber is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
estoque is highly correlated with endividamento and 3 other fieldsHigh correlation
faturamentoBruto is highly correlated with endividamento and 3 other fieldsHigh correlation
margemBruta is highly correlated with endividamento and 3 other fieldsHigh correlation
custos is highly correlated with endividamento and 3 other fieldsHigh correlation
capitalSocial is highly correlated with limiteEmpresaAnaliseCreditoHigh correlation
scorePontualidade is highly correlated with margemBrutaAcumuladaHigh correlation
limiteEmpresaAnaliseCredito is highly correlated with capitalSocialHigh correlation
df_index is highly correlated with numero_solicitacaoHigh correlation
numero_solicitacao is highly correlated with df_indexHigh correlation
margemBrutaAcumulada is highly correlated with scorePontualidadeHigh correlation
prazoMedioRecebimentoVendas is highly correlated with titulosEmAbertoHigh correlation
titulosEmAberto is highly correlated with prazoMedioRecebimentoVendasHigh correlation
valorSolicitado is highly correlated with valorAprovado and 8 other fieldsHigh correlation
diferencaPercentualRisco is highly correlated with percentualRiscoHigh correlation
percentualRisco is highly correlated with diferencaPercentualRiscoHigh correlation
valorAprovado is highly correlated with valorSolicitado and 10 other fieldsHigh correlation
ativoCirculante is highly correlated with valorSolicitado and 12 other fieldsHigh correlation
passivoCirculante is highly correlated with valorSolicitado and 11 other fieldsHigh correlation
totalAtivo is highly correlated with valorSolicitado and 12 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with valorAprovado and 9 other fieldsHigh correlation
endividamento is highly correlated with ativoCirculante and 3 other fieldsHigh correlation
duplicatasAReceber is highly correlated with valorSolicitado and 11 other fieldsHigh correlation
estoque is highly correlated with valorSolicitado and 11 other fieldsHigh correlation
faturamentoBruto is highly correlated with valorSolicitado and 10 other fieldsHigh correlation
margemBruta is highly correlated with valorSolicitado and 9 other fieldsHigh correlation
custos is highly correlated with valorSolicitado and 9 other fieldsHigh correlation
capitalSocial is highly correlated with ativoCirculante and 5 other fieldsHigh correlation
scorePontualidade is highly correlated with margemBrutaAcumuladaHigh correlation
limiteEmpresaAnaliseCredito is highly correlated with valorAprovado and 4 other fieldsHigh correlation
df_index is highly correlated with numero_solicitacaoHigh correlation
numero_solicitacao is highly correlated with df_indexHigh correlation
prazoMedioRecebimentoVendas is highly correlated with titulosEmAbertoHigh correlation
titulosEmAberto is highly correlated with prazoMedioRecebimentoVendasHigh correlation
valorSolicitado is highly correlated with valorAprovadoHigh correlation
diferencaPercentualRisco is highly correlated with percentualRiscoHigh correlation
percentualRisco is highly correlated with diferencaPercentualRiscoHigh correlation
valorAprovado is highly correlated with valorSolicitado and 1 other fieldsHigh correlation
ativoCirculante is highly correlated with passivoCirculante and 7 other fieldsHigh correlation
passivoCirculante is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
totalAtivo is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with ativoCirculante and 4 other fieldsHigh correlation
duplicatasAReceber is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
estoque is highly correlated with ativoCirculante and 7 other fieldsHigh correlation
faturamentoBruto is highly correlated with valorAprovado and 7 other fieldsHigh correlation
margemBruta is highly correlated with ativoCirculante and 6 other fieldsHigh correlation
custos is highly correlated with ativoCirculante and 6 other fieldsHigh correlation
totalPatrimonioLiquido is highly correlated with anoFundacao and 7 other fieldsHigh correlation
anoFundacao is highly correlated with totalPatrimonioLiquido and 5 other fieldsHigh correlation
capitalSocial is highly correlated with totalPatrimonioLiquido and 4 other fieldsHigh correlation
empresa_MeEppMei is highly correlated with diferencaPercentualRisco and 2 other fieldsHigh correlation
endividamento is highly correlated with capitalSocial and 5 other fieldsHigh correlation
diferencaPercentualRisco is highly correlated with empresa_MeEppMei and 2 other fieldsHigh correlation
ativoCirculante is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
duplicatasAReceber is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
margemBrutaAcumulada is highly correlated with scorePontualidadeHigh correlation
limiteEmpresaAnaliseCredito is highly correlated with capitalSocial and 5 other fieldsHigh correlation
scorePontualidade is highly correlated with margemBrutaAcumuladaHigh correlation
intervaloFundacao is highly correlated with definicaoRiscoHigh correlation
percentualRisco is highly correlated with empresa_MeEppMei and 2 other fieldsHigh correlation
margemBruta is highly correlated with endividamento and 4 other fieldsHigh correlation
df_index is highly correlated with numero_solicitacaoHigh correlation
faturamentoBruto is highly correlated with totalPatrimonioLiquido and 11 other fieldsHigh correlation
definicaoRisco is highly correlated with empresa_MeEppMei and 3 other fieldsHigh correlation
estoque is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
custos is highly correlated with endividamento and 8 other fieldsHigh correlation
totalAtivo is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
numero_solicitacao is highly correlated with df_indexHigh correlation
passivoCirculante is highly correlated with totalPatrimonioLiquido and 6 other fieldsHigh correlation
percentualProtestos has 1334 (16.3%) missing values Missing
primeiraCompra has 86 (1.1%) missing values Missing
valorAprovado has 611 (7.5%) missing values Missing
dataAprovadoEmComite has 7622 (93.2%) missing values Missing
periodoBalanco has 3484 (42.6%) missing values Missing
ativoCirculante has 3484 (42.6%) missing values Missing
passivoCirculante has 3484 (42.6%) missing values Missing
totalAtivo has 3484 (42.6%) missing values Missing
totalPatrimonioLiquido has 3484 (42.6%) missing values Missing
endividamento has 3484 (42.6%) missing values Missing
duplicatasAReceber has 3484 (42.6%) missing values Missing
estoque has 3484 (42.6%) missing values Missing
dataAprovadoNivelAnalista has 1169 (14.3%) missing values Missing
percentualProtestos is highly skewed (γ1 = 50.923999) Skewed
valorSolicitado is highly skewed (γ1 = 70.53613279) Skewed
ativoCirculante is highly skewed (γ1 = 51.9597739) Skewed
passivoCirculante is highly skewed (γ1 = 43.91583933) Skewed
totalAtivo is highly skewed (γ1 = 51.60937065) Skewed
totalPatrimonioLiquido is highly skewed (γ1 = 36.3371307) Skewed
duplicatasAReceber is highly skewed (γ1 = 64.64324689) Skewed
anoFundacao is highly skewed (γ1 = -34.08083647) Skewed
limiteEmpresaAnaliseCredito is highly skewed (γ1 = 51.10302157) Skewed
dataAprovadoEmComite is uniformly distributed Uniform
dataAprovadoNivelAnalista is uniformly distributed Uniform
df_index has unique values Unique
numero_solicitacao has unique values Unique
maiorAtraso has 1583 (19.4%) zeros Zeros
margemBrutaAcumulada has 1400 (17.1%) zeros Zeros
percentualProtestos has 6826 (83.4%) zeros Zeros
prazoMedioRecebimentoVendas has 5036 (61.6%) zeros Zeros
titulosEmAberto has 4581 (56.0%) zeros Zeros
dashboardCorrelacao has 4819 (58.9%) zeros Zeros
ativoCirculante has 550 (6.7%) zeros Zeros
passivoCirculante has 587 (7.2%) zeros Zeros
totalAtivo has 553 (6.8%) zeros Zeros
totalPatrimonioLiquido has 585 (7.2%) zeros Zeros
endividamento has 2366 (28.9%) zeros Zeros
duplicatasAReceber has 1025 (12.5%) zeros Zeros
estoque has 758 (9.3%) zeros Zeros
faturamentoBruto has 411 (5.0%) zeros Zeros
margemBruta has 4300 (52.6%) zeros Zeros
custos has 4413 (53.9%) zeros Zeros
capitalSocial has 126 (1.5%) zeros Zeros
scorePontualidade has 1380 (16.9%) zeros Zeros
limiteEmpresaAnaliseCredito has 522 (6.4%) zeros Zeros

Reproduction

Analysis started2021-08-21 20:28:41.244839
Analysis finished2021-08-21 20:30:15.266328
Duration1 minute and 34.02 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4422.26687
Minimum0
Maximum8963
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:15.330263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile439.95
Q12164.75
median4392.5
Q36673.5
95-th percentile8507.05
Maximum8963
Range8963
Interquartile range (IQR)4508.75

Descriptive statistics

Standard deviation2594.830325
Coefficient of variation (CV)0.5867647523
Kurtosis-1.208763005
Mean4422.26687
Median Absolute Deviation (MAD)2255
Skewness0.03173630819
Sum36174143
Variance6733144.415
MonotonicityStrictly increasing
2021-08-21T17:30:15.449263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
67421
 
< 0.1%
6211
 
< 0.1%
26681
 
< 0.1%
88091
 
< 0.1%
47111
 
< 0.1%
67581
 
< 0.1%
6131
 
< 0.1%
26601
 
< 0.1%
47031
 
< 0.1%
Other values (8170)8170
99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
89631
< 0.1%
89611
< 0.1%
89601
< 0.1%
89591
< 0.1%
89581
< 0.1%
89571
< 0.1%
89561
< 0.1%
89551
< 0.1%
89541
< 0.1%
89531
< 0.1%

numero_solicitacao
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4485.965526
Minimum1
Maximum9036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:15.569262image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile457.95
Q12237.75
median4465.5
Q36746.5
95-th percentile8580.05
Maximum9036
Range9035
Interquartile range (IQR)4508.75

Descriptive statistics

Standard deviation2608.126506
Coefficient of variation (CV)0.5813969125
Kurtosis-1.201243877
Mean4485.965526
Median Absolute Deviation (MAD)2255
Skewness0.01978034185
Sum36695198
Variance6802323.873
MonotonicityStrictly increasing
2021-08-21T17:30:15.687297image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
26281
 
< 0.1%
87931
 
< 0.1%
46951
 
< 0.1%
67421
 
< 0.1%
5971
 
< 0.1%
26441
 
< 0.1%
46871
 
< 0.1%
67341
 
< 0.1%
5891
 
< 0.1%
Other values (8170)8170
99.9%
ValueCountFrequency (%)
11
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
90361
< 0.1%
90341
< 0.1%
90331
< 0.1%
90321
< 0.1%
90311
< 0.1%
90301
< 0.1%
90291
< 0.1%
90281
< 0.1%
90271
< 0.1%
90261
< 0.1%

razaoSocial
Categorical

HIGH CARDINALITY

Distinct2725
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
Suzanne Smith
 
25
Mr. Mohamed Howard
 
20
Dr. Vanessa Bird
 
20
Kelly Fox
 
19
Douglas Taylor
 
18
Other values (2720)
8078 

Length

Max length28
Median length14
Mean length14.86870416
Min length7

Characters and Unicode

Total characters121626
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique935 ?
Unique (%)11.4%

Sample

1st rowJames Richardson-Patel
2nd rowJoanna Hudson
3rd rowGordon Jones-Hopkins
4th rowNigel Lee
5th rowLiam Jackson

Common Values

ValueCountFrequency (%)
Suzanne Smith25
 
0.3%
Mr. Mohamed Howard20
 
0.2%
Dr. Vanessa Bird20
 
0.2%
Kelly Fox19
 
0.2%
Douglas Taylor18
 
0.2%
Malcolm Bolton18
 
0.2%
Brett Wheeler17
 
0.2%
Dr. Jake Dale16
 
0.2%
Ms. Pauline Hunter15
 
0.2%
Gail Wells15
 
0.2%
Other values (2715)7997
97.8%

Length

2021-08-21T17:30:15.951262image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr909
 
4.8%
mr634
 
3.4%
ms314
 
1.7%
mrs287
 
1.5%
miss261
 
1.4%
smith254
 
1.4%
jones210
 
1.1%
taylor127
 
0.7%
thomas100
 
0.5%
davies83
 
0.4%
Other values (1169)15586
83.1%

Most occurring characters

ValueCountFrequency (%)
10585
 
8.7%
e10431
 
8.6%
a9812
 
8.1%
r9692
 
8.0%
n8375
 
6.9%
o6823
 
5.6%
i6502
 
5.3%
l6311
 
5.2%
s6064
 
5.0%
t3845
 
3.2%
Other values (44)43186
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter87570
72.0%
Uppercase Letter20068
 
16.5%
Space Separator10585
 
8.7%
Other Punctuation2189
 
1.8%
Dash Punctuation1214
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10431
11.9%
a9812
11.2%
r9692
11.1%
n8375
9.6%
o6823
7.8%
i6502
7.4%
l6311
 
7.2%
s6064
 
6.9%
t3845
 
4.4%
h3627
 
4.1%
Other values (16)16088
18.4%
Uppercase Letter
ValueCountFrequency (%)
M2901
14.5%
D1937
 
9.7%
J1531
 
7.6%
S1511
 
7.5%
B1362
 
6.8%
C1190
 
5.9%
H1132
 
5.6%
R1046
 
5.2%
A1012
 
5.0%
W1010
 
5.0%
Other values (14)5436
27.1%
Other Punctuation
ValueCountFrequency (%)
.2144
97.9%
'45
 
2.1%
Space Separator
ValueCountFrequency (%)
10585
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107638
88.5%
Common13988
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10431
 
9.7%
a9812
 
9.1%
r9692
 
9.0%
n8375
 
7.8%
o6823
 
6.3%
i6502
 
6.0%
l6311
 
5.9%
s6064
 
5.6%
t3845
 
3.6%
h3627
 
3.4%
Other values (40)36156
33.6%
Common
ValueCountFrequency (%)
10585
75.7%
.2144
 
15.3%
-1214
 
8.7%
'45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII121626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10585
 
8.7%
e10431
 
8.6%
a9812
 
8.1%
r9692
 
8.0%
n8375
 
6.9%
o6823
 
5.6%
i6502
 
5.3%
l6311
 
5.2%
s6064
 
5.0%
t3845
 
3.2%
Other values (44)43186
35.5%

nomeFantasia
Categorical

HIGH CARDINALITY

Distinct2643
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
Nathan Jones
 
32
Marian Day
 
25
Linda Bradley
 
20
Anne Payne
 
20
Lorraine Hughes
 
19
Other values (2638)
8064 

Length

Max length28
Median length15
Mean length14.91308068
Min length8

Characters and Unicode

Total characters121989
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique869 ?
Unique (%)10.6%

Sample

1st rowAlexandra Williams
2nd rowDr. David Rees
3rd rowSara Reid-Robson
4th rowDr. Stanley Duncan
5th rowGerard Gordon

Common Values

ValueCountFrequency (%)
Nathan Jones32
 
0.4%
Marian Day25
 
0.3%
Linda Bradley20
 
0.2%
Anne Payne20
 
0.2%
Lorraine Hughes19
 
0.2%
Dale Lowe18
 
0.2%
Linda Cook16
 
0.2%
Mr. Ricky Williams16
 
0.2%
Marilyn Holden15
 
0.2%
Janet Owen15
 
0.2%
Other values (2633)7984
97.6%

Length

2021-08-21T17:30:16.204296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr923
 
4.9%
mr584
 
3.1%
mrs306
 
1.6%
ms291
 
1.6%
miss263
 
1.4%
jones198
 
1.1%
smith177
 
0.9%
williams127
 
0.7%
thomas87
 
0.5%
taylor85
 
0.5%
Other values (1163)15686
83.8%

Most occurring characters

ValueCountFrequency (%)
10547
 
8.6%
e10499
 
8.6%
a9779
 
8.0%
r9456
 
7.8%
n8930
 
7.3%
o6768
 
5.5%
i6723
 
5.5%
l6634
 
5.4%
s5946
 
4.9%
t3860
 
3.2%
Other values (44)42847
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter88117
72.2%
Uppercase Letter19994
 
16.4%
Space Separator10547
 
8.6%
Other Punctuation2164
 
1.8%
Dash Punctuation1167
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10499
11.9%
a9779
11.1%
r9456
10.7%
n8930
10.1%
o6768
7.7%
i6723
7.6%
l6634
7.5%
s5946
 
6.7%
t3860
 
4.4%
h3398
 
3.9%
Other values (16)16124
18.3%
Uppercase Letter
ValueCountFrequency (%)
M2696
13.5%
D1933
 
9.7%
J1488
 
7.4%
S1439
 
7.2%
B1331
 
6.7%
C1261
 
6.3%
H1119
 
5.6%
A1083
 
5.4%
W963
 
4.8%
R943
 
4.7%
Other values (14)5738
28.7%
Other Punctuation
ValueCountFrequency (%)
.2104
97.2%
'60
 
2.8%
Space Separator
ValueCountFrequency (%)
10547
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin108111
88.6%
Common13878
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10499
 
9.7%
a9779
 
9.0%
r9456
 
8.7%
n8930
 
8.3%
o6768
 
6.3%
i6723
 
6.2%
l6634
 
6.1%
s5946
 
5.5%
t3860
 
3.6%
h3398
 
3.1%
Other values (40)36118
33.4%
Common
ValueCountFrequency (%)
10547
76.0%
.2104
 
15.2%
-1167
 
8.4%
'60
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII121989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10547
 
8.6%
e10499
 
8.6%
a9779
 
8.0%
r9456
 
7.8%
n8930
 
7.3%
o6768
 
5.5%
i6723
 
5.5%
l6634
 
5.4%
s5946
 
4.9%
t3860
 
3.2%
Other values (44)42847
35.1%

cnpjSemTraco
Categorical

HIGH CARDINALITY

Distinct2789
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
AVAO63044598911311
 
25
VVSW90409251685348
 
20
GTPO06511661214973
 
20
DNLY35380748067028
 
19
JXCH36268697453955
 
18
Other values (2784)
8078 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters147240
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique965 ?
Unique (%)11.8%

Sample

1st rowKEBE17609492220843
2nd rowGCVQ28531614261293
3rd rowKJND32266018316396
4th rowCGQN15826802440348
5th rowKAYS53232027306925

Common Values

ValueCountFrequency (%)
AVAO6304459891131125
 
0.3%
VVSW9040925168534820
 
0.2%
GTPO0651166121497320
 
0.2%
DNLY3538074806702819
 
0.2%
JXCH3626869745395518
 
0.2%
SPID0756721273863916
 
0.2%
OEIN3715451682806415
 
0.2%
YFOX8908108327545215
 
0.2%
HXUT1126425410802114
 
0.2%
DSDP4291529621354114
 
0.2%
Other values (2779)8004
97.8%

Length

2021-08-21T17:30:16.453299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
avao6304459891131125
 
0.3%
gtpo0651166121497320
 
0.2%
vvsw9040925168534820
 
0.2%
dnly3538074806702819
 
0.2%
jxch3626869745395518
 
0.2%
spid0756721273863916
 
0.2%
oein3715451682806415
 
0.2%
yfox8908108327545215
 
0.2%
svin4071535281347014
 
0.2%
dsdp4291529621354114
 
0.2%
Other values (2779)8004
97.8%

Most occurring characters

ValueCountFrequency (%)
112054
 
8.2%
511598
 
7.9%
411534
 
7.8%
611527
 
7.8%
211437
 
7.8%
711405
 
7.7%
311354
 
7.7%
911331
 
7.7%
811240
 
7.6%
011040
 
7.5%
Other values (26)32720
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number114520
77.8%
Uppercase Letter32720
 
22.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V1416
 
4.3%
H1406
 
4.3%
Y1388
 
4.2%
A1360
 
4.2%
O1356
 
4.1%
X1337
 
4.1%
Q1312
 
4.0%
N1310
 
4.0%
J1305
 
4.0%
B1299
 
4.0%
Other values (16)19231
58.8%
Decimal Number
ValueCountFrequency (%)
112054
10.5%
511598
10.1%
411534
10.1%
611527
10.1%
211437
10.0%
711405
10.0%
311354
9.9%
911331
9.9%
811240
9.8%
011040
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common114520
77.8%
Latin32720
 
22.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
V1416
 
4.3%
H1406
 
4.3%
Y1388
 
4.2%
A1360
 
4.2%
O1356
 
4.1%
X1337
 
4.1%
Q1312
 
4.0%
N1310
 
4.0%
J1305
 
4.0%
B1299
 
4.0%
Other values (16)19231
58.8%
Common
ValueCountFrequency (%)
112054
10.5%
511598
10.1%
411534
10.1%
611527
10.1%
211437
10.0%
711405
10.0%
311354
9.9%
911331
9.9%
811240
9.8%
011040
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII147240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112054
 
8.2%
511598
 
7.9%
411534
 
7.8%
611527
 
7.8%
211437
 
7.8%
711405
 
7.7%
311354
 
7.7%
911331
 
7.7%
811240
 
7.6%
011040
 
7.5%
Other values (26)32720
22.2%

maiorAtraso
Real number (ℝ≥0)

ZEROS

Distinct174
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.35244499
Minimum0
Maximum1265
Zeros1583
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:16.555299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q322
95-th percentile94
Maximum1265
Range1265
Interquartile range (IQR)19

Descriptive statistics

Standard deviation65.48454448
Coefficient of variation (CV)2.689033668
Kurtosis95.95809377
Mean24.35244499
Median Absolute Deviation (MAD)6
Skewness8.484521021
Sum199203
Variance4288.225566
MonotonicityNot monotonic
2021-08-21T17:30:16.683300image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01583
19.4%
3946
 
11.6%
4667
 
8.2%
5411
 
5.0%
6345
 
4.2%
2255
 
3.1%
7202
 
2.5%
8191
 
2.3%
18175
 
2.1%
14159
 
1.9%
Other values (164)3246
39.7%
ValueCountFrequency (%)
01583
19.4%
111
 
0.1%
2255
 
3.1%
3946
11.6%
4667
8.2%
5411
 
5.0%
6345
 
4.2%
7202
 
2.5%
8191
 
2.3%
9158
 
1.9%
ValueCountFrequency (%)
12651
 
< 0.1%
9775
0.1%
8071
 
< 0.1%
7945
0.1%
7798
0.1%
7404
< 0.1%
6904
< 0.1%
6293
 
< 0.1%
6142
 
< 0.1%
5893
 
< 0.1%

margemBrutaAcumulada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2130
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3645478319
Minimum0
Maximum1
Zeros1400
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:16.806924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2865927592
median0.4059044375
Q30.508244854
95-th percentile0.6247772648
Maximum1
Range1
Interquartile range (IQR)0.2216520948

Descriptive statistics

Standard deviation0.2002107907
Coefficient of variation (CV)0.5492030761
Kurtosis-0.4462978225
Mean0.3645478319
Median Absolute Deviation (MAD)0.1084825522
Skewness-0.6572540692
Sum2982.001265
Variance0.04008436071
MonotonicityNot monotonic
2021-08-21T17:30:16.934864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01400
 
17.1%
0.565045773925
 
0.3%
0.371676033420
 
0.2%
0.412316285720
 
0.2%
0.478865639519
 
0.2%
0.374572665618
 
0.2%
0.470681428116
 
0.2%
0.530658503415
 
0.2%
0.444543829315
 
0.2%
0.383139342914
 
0.2%
Other values (2120)6618
80.9%
ValueCountFrequency (%)
01400
17.1%
1.20858 × 10-54
 
< 0.1%
0.00400164313
 
< 0.1%
0.01186610291
 
< 0.1%
0.01986093912
 
< 0.1%
0.02167777184
 
< 0.1%
0.02798962962
 
< 0.1%
0.04088205714
 
< 0.1%
0.04862851572
 
< 0.1%
0.05464732552
 
< 0.1%
ValueCountFrequency (%)
16
0.1%
0.93758237752
 
< 0.1%
0.89466822431
 
< 0.1%
0.83487434781
 
< 0.1%
0.82575231484
< 0.1%
0.80635524621
 
< 0.1%
0.8002998852
 
< 0.1%
0.7758119531
 
< 0.1%
0.76840772235
0.1%
0.75785694443
< 0.1%

percentualProtestos
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct10
Distinct (%)0.1%
Missing1334
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean0.01611736048
Minimum0
Maximum36.98372833
Zeros6826
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:17.035899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36.98372833
Range36.98372833
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5613652674
Coefficient of variation (CV)34.82985121
Kurtosis2991.31317
Mean0.01611736048
Median Absolute Deviation (MAD)0
Skewness50.923999
Sum110.3394498
Variance0.3151309635
MonotonicityNot monotonic
2021-08-21T17:30:17.116215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
06826
83.4%
0.955607843
 
< 0.1%
0.590745863
 
< 0.1%
1.70257633
 
< 0.1%
15.298109553
 
< 0.1%
0.495062323
 
< 0.1%
3.035994822
 
< 0.1%
6.769564181
 
< 0.1%
3.387862091
 
< 0.1%
36.983728331
 
< 0.1%
(Missing)1334
 
16.3%
ValueCountFrequency (%)
06826
83.4%
0.495062323
 
< 0.1%
0.590745863
 
< 0.1%
0.955607843
 
< 0.1%
1.70257633
 
< 0.1%
3.035994822
 
< 0.1%
3.387862091
 
< 0.1%
6.769564181
 
< 0.1%
15.298109553
 
< 0.1%
36.983728331
 
< 0.1%
ValueCountFrequency (%)
36.983728331
 
< 0.1%
15.298109553
 
< 0.1%
6.769564181
 
< 0.1%
3.387862091
 
< 0.1%
3.035994822
 
< 0.1%
1.70257633
 
< 0.1%
0.955607843
 
< 0.1%
0.590745863
 
< 0.1%
0.495062323
 
< 0.1%
06826
83.4%

primeiraCompra
Categorical

HIGH CARDINALITY
MISSING

Distinct1927
Distinct (%)23.8%
Missing86
Missing (%)1.1%
Memory size64.0 KiB
2012-12-21T00:00:00
 
31
2019-12-17T00:00:00
 
25
2017-09-04T00:00:00
 
24
2011-08-02T00:00:00
 
23
2020-05-25T00:00:00
 
23
Other values (1922)
7968 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters153786
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique493 ?
Unique (%)6.1%

Sample

1st row2015-12-10T00:00:00
2nd row2019-11-27T00:00:00
3rd row2017-02-13T17:20:27
4th row2010-07-13T00:00:00
5th row2019-06-28T10:25:57

Common Values

ValueCountFrequency (%)
2012-12-21T00:00:0031
 
0.4%
2019-12-17T00:00:0025
 
0.3%
2017-09-04T00:00:0024
 
0.3%
2011-08-02T00:00:0023
 
0.3%
2020-05-25T00:00:0023
 
0.3%
2019-07-29T00:00:0021
 
0.3%
2016-04-20T00:00:0021
 
0.3%
2018-10-17T00:00:0021
 
0.3%
2018-12-05T00:00:0020
 
0.2%
2015-08-27T00:00:0020
 
0.2%
Other values (1917)7865
96.1%
(Missing)86
 
1.1%

Length

2021-08-21T17:30:17.348271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-12-21t00:00:0031
 
0.4%
2019-12-17t00:00:0025
 
0.3%
2017-09-04t00:00:0024
 
0.3%
2020-05-25t00:00:0023
 
0.3%
2011-08-02t00:00:0023
 
0.3%
2016-04-20t00:00:0021
 
0.3%
2018-10-17t00:00:0021
 
0.3%
2019-07-29t00:00:0021
 
0.3%
2006-06-01t00:00:0020
 
0.2%
2015-08-27t00:00:0020
 
0.2%
Other values (1917)7865
97.2%

Most occurring characters

ValueCountFrequency (%)
061790
40.2%
-16188
 
10.5%
:16188
 
10.5%
115180
 
9.9%
214848
 
9.7%
T8094
 
5.3%
73462
 
2.3%
33337
 
2.2%
93243
 
2.1%
83223
 
2.1%
Other values (3)8233
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number113316
73.7%
Dash Punctuation16188
 
10.5%
Other Punctuation16188
 
10.5%
Uppercase Letter8094
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
061790
54.5%
115180
 
13.4%
214848
 
13.1%
73462
 
3.1%
33337
 
2.9%
93243
 
2.9%
83223
 
2.8%
63080
 
2.7%
52592
 
2.3%
42561
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
-16188
100.0%
Uppercase Letter
ValueCountFrequency (%)
T8094
100.0%
Other Punctuation
ValueCountFrequency (%)
:16188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common145692
94.7%
Latin8094
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
061790
42.4%
-16188
 
11.1%
:16188
 
11.1%
115180
 
10.4%
214848
 
10.2%
73462
 
2.4%
33337
 
2.3%
93243
 
2.2%
83223
 
2.2%
63080
 
2.1%
Other values (2)5153
 
3.5%
Latin
ValueCountFrequency (%)
T8094
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII153786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
061790
40.2%
-16188
 
10.5%
:16188
 
10.5%
115180
 
9.9%
214848
 
9.7%
T8094
 
5.3%
73462
 
2.3%
33337
 
2.2%
93243
 
2.1%
83223
 
2.1%
Other values (3)8233
 
5.4%

prazoMedioRecebimentoVendas
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct180
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.66393643
Minimum0
Maximum1605
Zeros5036
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:17.451305image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330
95-th percentile95
Maximum1605
Range1605
Interquartile range (IQR)30

Descriptive statistics

Standard deviation67.56924978
Coefficient of variation (CV)2.981355423
Kurtosis242.2520383
Mean22.66393643
Median Absolute Deviation (MAD)0
Skewness12.42500088
Sum185391
Variance4565.603516
MonotonicityNot monotonic
2021-08-21T17:30:17.567372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05036
61.6%
4281
 
1.0%
3475
 
0.9%
3270
 
0.9%
5270
 
0.9%
3969
 
0.8%
3768
 
0.8%
2664
 
0.8%
2764
 
0.8%
1863
 
0.8%
Other values (170)2520
30.8%
ValueCountFrequency (%)
05036
61.6%
120
 
0.2%
231
 
0.4%
318
 
0.2%
436
 
0.4%
522
 
0.3%
641
 
0.5%
78
 
0.1%
859
 
0.7%
926
 
0.3%
ValueCountFrequency (%)
16056
0.1%
72313
0.2%
50711
0.1%
3574
 
< 0.1%
3552
 
< 0.1%
3452
 
< 0.1%
3443
 
< 0.1%
3415
 
0.1%
3381
 
< 0.1%
3281
 
< 0.1%

titulosEmAberto
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct759
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63004.50279
Minimum0
Maximum3938589.7
Zeros4581
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:17.698440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316522.5
95-th percentile362410.6
Maximum3938589.7
Range3938589.7
Interquartile range (IQR)16522.5

Descriptive statistics

Standard deviation240681.9504
Coefficient of variation (CV)3.820075387
Kurtosis78.97429162
Mean63004.50279
Median Absolute Deviation (MAD)0
Skewness7.57305122
Sum515376832.8
Variance5.792780124 × 1010
MonotonicityNot monotonic
2021-08-21T17:30:17.827589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04581
56.0%
118195.8725
 
0.3%
2079720
 
0.2%
17648020
 
0.2%
11337019
 
0.2%
90290.918
 
0.2%
5760018
 
0.2%
529387.0416
 
0.2%
427368.6815
 
0.2%
246173.715
 
0.2%
Other values (749)3433
42.0%
ValueCountFrequency (%)
04581
56.0%
12.054
 
< 0.1%
3156
 
0.1%
533.345
 
0.1%
6604
 
< 0.1%
693.336
 
0.1%
711.11
 
< 0.1%
7764
 
< 0.1%
8405
 
0.1%
894.41
 
< 0.1%
ValueCountFrequency (%)
3938589.75
 
0.1%
283665610
0.1%
2140954.393
 
< 0.1%
1985132.9112
0.1%
1913477.537
0.1%
1693918.997
0.1%
1491736.3714
0.2%
13744501
 
< 0.1%
1276462.668
0.1%
1180370.618
0.1%

valorSolicitado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct344
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean468327.6696
Minimum100
Maximum1200000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:17.952667image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile10000
Q120000
median50000
Q3120000
95-th percentile1000105
Maximum1200000000
Range1199999900
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation15000824.18
Coefficient of variation (CV)32.03061692
Kurtosis5313.745081
Mean468327.6696
Median Absolute Deviation (MAD)30000
Skewness70.53613279
Sum3830920337
Variance2.25024726 × 1014
MonotonicityNot monotonic
2021-08-21T17:30:18.085781image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000998
 
12.2%
20000822
 
10.0%
30000740
 
9.0%
15000467
 
5.7%
40000465
 
5.7%
10000464
 
5.7%
100000382
 
4.7%
25000227
 
2.8%
150000199
 
2.4%
200000177
 
2.2%
Other values (334)3239
39.6%
ValueCountFrequency (%)
1001
 
< 0.1%
16001
 
< 0.1%
25001
 
< 0.1%
29001
 
< 0.1%
30008
 
0.1%
40003
 
< 0.1%
5000129
1.6%
52001
 
< 0.1%
54001
 
< 0.1%
55004
 
< 0.1%
ValueCountFrequency (%)
12000000001
< 0.1%
6000000001
< 0.1%
1500000001
< 0.1%
1287000001
< 0.1%
107000001
< 0.1%
93000001
< 0.1%
92000001
< 0.1%
80000001
< 0.1%
70500001
< 0.1%
69000002
< 0.1%

status
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
AprovadoAnalista
7011 
ReprovadoAnalista
 
590
AprovadoComite
 
558
ReprovadoComite
 
20
AguardandoAprovacao
 
1

Length

Max length19
Median length16
Mean length15.93361858
Min length14

Characters and Unicode

Total characters130337
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAprovadoAnalista
2nd rowAprovadoAnalista
3rd rowAprovadoAnalista
4th rowAprovadoAnalista
5th rowAprovadoAnalista

Common Values

ValueCountFrequency (%)
AprovadoAnalista7011
85.7%
ReprovadoAnalista590
 
7.2%
AprovadoComite558
 
6.8%
ReprovadoComite20
 
0.2%
AguardandoAprovacao1
 
< 0.1%

Length

2021-08-21T17:30:18.308859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-21T17:30:18.373861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
aprovadoanalista7011
85.7%
reprovadoanalista590
 
7.2%
aprovadocomite558
 
6.8%
reprovadocomite20
 
0.2%
aguardandoaprovacao1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a23385
17.9%
o16939
13.0%
A15172
11.6%
r8181
 
6.3%
d8181
 
6.3%
p8180
 
6.3%
v8180
 
6.3%
i8179
 
6.3%
t8179
 
6.3%
n7602
 
5.8%
Other values (9)18159
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter113977
87.4%
Uppercase Letter16360
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a23385
20.5%
o16939
14.9%
r8181
 
7.2%
d8181
 
7.2%
p8180
 
7.2%
v8180
 
7.2%
i8179
 
7.2%
t8179
 
7.2%
n7602
 
6.7%
l7601
 
6.7%
Other values (6)9370
8.2%
Uppercase Letter
ValueCountFrequency (%)
A15172
92.7%
R610
 
3.7%
C578
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin130337
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a23385
17.9%
o16939
13.0%
A15172
11.6%
r8181
 
6.3%
d8181
 
6.3%
p8180
 
6.3%
v8180
 
6.3%
i8179
 
6.3%
t8179
 
6.3%
n7602
 
5.8%
Other values (9)18159
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII130337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a23385
17.9%
o16939
13.0%
A15172
11.6%
r8181
 
6.3%
d8181
 
6.3%
p8180
 
6.3%
v8180
 
6.3%
i8179
 
6.3%
t8179
 
6.3%
n7602
 
5.8%
Other values (9)18159
13.9%

definicaoRisco
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
De 11 a 30 % - Baixo
4457 
De 31 a 50 % - Médio
2499 
De 0 a 10 % - Muito Baixo
840 
De 51 a 80 % - Alto
 
384

Length

Max length25
Median length20
Mean length20.46650367
Min length19

Characters and Unicode

Total characters167416
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDe 11 a 30 % - Baixo
2nd rowDe 11 a 30 % - Baixo
3rd rowDe 51 a 80 % - Alto
4th rowDe 11 a 30 % - Baixo
5th rowDe 31 a 50 % - Médio

Common Values

ValueCountFrequency (%)
De 11 a 30 % - Baixo4457
54.5%
De 31 a 50 % - Médio2499
30.6%
De 0 a 10 % - Muito Baixo840
 
10.3%
De 51 a 80 % - Alto384
 
4.7%

Length

2021-08-21T17:30:18.569909image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-21T17:30:18.636915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
16360
28.2%
a8180
14.1%
de8180
14.1%
baixo5297
 
9.1%
304457
 
7.7%
114457
 
7.7%
312499
 
4.3%
502499
 
4.3%
médio2499
 
4.3%
10840
 
1.4%
Other values (5)2832
 
4.9%

Most occurring characters

ValueCountFrequency (%)
49920
29.8%
a13477
 
8.1%
112637
 
7.5%
09020
 
5.4%
o9020
 
5.4%
i8636
 
5.2%
D8180
 
4.9%
e8180
 
4.9%
%8180
 
4.9%
-8180
 
4.9%
Other values (12)31986
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52056
31.1%
Space Separator49920
29.8%
Decimal Number31880
19.0%
Uppercase Letter17200
 
10.3%
Other Punctuation8180
 
4.9%
Dash Punctuation8180
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a13477
25.9%
o9020
17.3%
i8636
16.6%
e8180
15.7%
x5297
 
10.2%
é2499
 
4.8%
d2499
 
4.8%
t1224
 
2.4%
u840
 
1.6%
l384
 
0.7%
Decimal Number
ValueCountFrequency (%)
112637
39.6%
09020
28.3%
36956
21.8%
52883
 
9.0%
8384
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
D8180
47.6%
B5297
30.8%
M3339
19.4%
A384
 
2.2%
Space Separator
ValueCountFrequency (%)
49920
100.0%
Other Punctuation
ValueCountFrequency (%)
%8180
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common98160
58.6%
Latin69256
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a13477
19.5%
o9020
13.0%
i8636
12.5%
D8180
11.8%
e8180
11.8%
B5297
 
7.6%
x5297
 
7.6%
M3339
 
4.8%
é2499
 
3.6%
d2499
 
3.6%
Other values (4)2832
 
4.1%
Common
ValueCountFrequency (%)
49920
50.9%
112637
 
12.9%
09020
 
9.2%
%8180
 
8.3%
-8180
 
8.3%
36956
 
7.1%
52883
 
2.9%
8384
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII164917
98.5%
Latin 1 Sup2499
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49920
30.3%
a13477
 
8.2%
112637
 
7.7%
09020
 
5.5%
o9020
 
5.5%
i8636
 
5.2%
D8180
 
5.0%
e8180
 
5.0%
%8180
 
5.0%
-8180
 
5.0%
Other values (11)29487
17.9%
Latin 1 Sup
ValueCountFrequency (%)
é2499
100.0%

diferencaPercentualRisco
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct79
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7277568308
Minimum0.2075471698
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:18.734951image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.2075471698
5-th percentile0.4905660377
Q10.6428571429
median0.75
Q30.8392857143
95-th percentile0.9107142857
Maximum1
Range0.7924528302
Interquartile range (IQR)0.1964285714

Descriptive statistics

Standard deviation0.1308120914
Coefficient of variation (CV)0.1797469785
Kurtosis-0.2553272182
Mean0.7277568308
Median Absolute Deviation (MAD)0.1071428571
Skewness-0.4392367672
Sum5953.050876
Variance0.01711180327
MonotonicityNot monotonic
2021-08-21T17:30:18.859572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75766
 
9.4%
0.8571428571732
 
8.9%
0.8035714286587
 
7.2%
0.6428571429468
 
5.7%
0.6964285714424
 
5.2%
0.9107142857387
 
4.7%
0.6607142857234
 
2.9%
0.5357142857214
 
2.6%
0.7142857143208
 
2.5%
0.7857142857207
 
2.5%
Other values (69)3953
48.3%
ValueCountFrequency (%)
0.20754716982
 
< 0.1%
0.26415094341
 
< 0.1%
0.28301886791
 
< 0.1%
0.30188679253
 
< 0.1%
0.3207547174
 
< 0.1%
0.33928571438
0.1%
0.33962264159
0.1%
0.35714285713
 
< 0.1%
0.3584905664
 
< 0.1%
0.37511
0.1%
ValueCountFrequency (%)
116
 
0.2%
0.98214285713
 
< 0.1%
0.9642857143163
2.0%
0.962264150913
 
0.2%
0.946428571467
 
0.8%
0.943396226413
 
0.2%
0.928571428613
 
0.2%
0.92452830191
 
< 0.1%
0.9107142857387
4.7%
0.905660377422
 
0.3%

percentualRisco
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2722431692
Minimum0
Maximum0.7924528302
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:18.989860image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08928571429
Q10.1607142857
median0.25
Q30.3571428571
95-th percentile0.5094339623
Maximum0.7924528302
Range0.7924528302
Interquartile range (IQR)0.1964285714

Descriptive statistics

Standard deviation0.1308120914
Coefficient of variation (CV)0.4804972401
Kurtosis-0.2553272182
Mean0.2722431692
Median Absolute Deviation (MAD)0.1071428571
Skewness0.4392367672
Sum2226.949124
Variance0.01711180327
MonotonicityNot monotonic
2021-08-21T17:30:19.114897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1428571429732
 
8.9%
0.25665
 
8.1%
0.1964285714587
 
7.2%
0.3571428571468
 
5.7%
0.3035714286424
 
5.2%
0.08928571429387
 
4.7%
0.3392857143234
 
2.9%
0.4642857143214
 
2.6%
0.2857142857208
 
2.5%
0.2142857143207
 
2.5%
Other values (71)4054
49.6%
ValueCountFrequency (%)
016
 
0.2%
0.017857142863
 
< 0.1%
0.03571428571163
2.0%
0.0377358490613
 
0.2%
0.0535714285767
 
0.8%
0.0566037735813
 
0.2%
0.0714285714313
 
0.2%
0.075471698111
 
< 0.1%
0.08928571429387
4.7%
0.0943396226422
 
0.3%
ValueCountFrequency (%)
0.79245283022
 
< 0.1%
0.73584905661
 
< 0.1%
0.71698113211
 
< 0.1%
0.69811320753
 
< 0.1%
0.6792452834
< 0.1%
0.66071428578
0.1%
0.66037735859
0.1%
0.64285714293
 
< 0.1%
0.6415094344
< 0.1%
0.6251
 
< 0.1%

dashboardCorrelacao
Real number (ℝ)

ZEROS

Distinct701
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05097423174
Minimum-0.9999901219
Maximum0.9999901219
Zeros4819
Zeros (%)58.9%
Negative1398
Negative (%)17.1%
Memory size64.0 KiB
2021-08-21T17:30:19.245861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.9999901219
5-th percentile-0.8660254038
Q10
median0
Q30
95-th percentile0.8660254038
Maximum0.9999901219
Range1.999980244
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.492808559
Coefficient of variation (CV)9.667797674
Kurtosis0.1352137804
Mean0.05097423174
Median Absolute Deviation (MAD)0
Skewness0.0008599998283
Sum416.9692156
Variance0.2428602758
MonotonicityNot monotonic
2021-08-21T17:30:19.372864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04819
58.9%
0.8660254038819
 
10.0%
-0.8660254038346
 
4.2%
0.866025403879
 
1.0%
-0.866025403866
 
0.8%
3 × 10-1622
 
0.3%
-2 × 10-1617
 
0.2%
-1.1 × 10-1515
 
0.2%
0.94412020214
 
0.2%
-0.866025403814
 
0.2%
Other values (691)1969
24.1%
ValueCountFrequency (%)
-0.99999012195
0.1%
-0.99998540197
0.1%
-0.99998193582
 
< 0.1%
-0.99986814691
 
< 0.1%
-0.99981264671
 
< 0.1%
-0.99980828341
 
< 0.1%
-0.99973121381
 
< 0.1%
-0.99947900271
 
< 0.1%
-0.99944970431
 
< 0.1%
-0.99943194451
 
< 0.1%
ValueCountFrequency (%)
0.99999012191
 
< 0.1%
0.99992412266
0.1%
0.99992297543
< 0.1%
0.99983760923
< 0.1%
0.99980828344
< 0.1%
0.99976283721
 
< 0.1%
0.99968277231
 
< 0.1%
0.99966707113
< 0.1%
0.99953320334
< 0.1%
0.99947900271
 
< 0.1%

valorAprovado
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct326
Distinct (%)4.3%
Missing611
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean189792.577
Minimum0
Maximum10700000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:19.503863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5000
Q115100
median35000
Q3100000
95-th percentile1000000
Maximum10700000
Range10700000
Interquartile range (IQR)84900

Descriptive statistics

Standard deviation543518.5782
Coefficient of variation (CV)2.863750453
Kurtosis70.11249456
Mean189792.577
Median Absolute Deviation (MAD)25000
Skewness6.945329581
Sum1436540015
Variance2.954124448 × 1011
MonotonicityNot monotonic
2021-08-21T17:30:19.628827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000735
 
9.0%
10000730
 
8.9%
15000604
 
7.4%
30000582
 
7.1%
50000533
 
6.5%
40000375
 
4.6%
5000357
 
4.4%
25000295
 
3.6%
100000236
 
2.9%
35000202
 
2.5%
Other values (316)2920
35.7%
(Missing)611
 
7.5%
ValueCountFrequency (%)
04
< 0.1%
11
 
< 0.1%
102
 
< 0.1%
1201
 
< 0.1%
16001
 
< 0.1%
20001
 
< 0.1%
25001
 
< 0.1%
29001
 
< 0.1%
30008
0.1%
35001
 
< 0.1%
ValueCountFrequency (%)
107000001
 
< 0.1%
92000001
 
< 0.1%
69000002
< 0.1%
65500001
 
< 0.1%
65000003
< 0.1%
62000001
 
< 0.1%
60000002
< 0.1%
59000001
 
< 0.1%
55000001
 
< 0.1%
51600002
< 0.1%

dataAprovadoEmComite
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct558
Distinct (%)100.0%
Missing7622
Missing (%)93.2%
Memory size64.0 KiB
2020-03-06T19:23:22
 
1
2020-05-21T16:18:14
 
1
2020-03-13T17:40:48
 
1
2020-02-12T20:26:50
 
1
2020-02-12T20:14:14
 
1
Other values (553)
553 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters10602
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique558 ?
Unique (%)100.0%

Sample

1st row2020-02-05T19:14:37
2nd row2020-02-07T18:57:37
3rd row2020-02-21T12:42:19
4th row2020-02-12T20:24:50
5th row2020-02-12T20:45:57

Common Values

ValueCountFrequency (%)
2020-03-06T19:23:221
 
< 0.1%
2020-05-21T16:18:141
 
< 0.1%
2020-03-13T17:40:481
 
< 0.1%
2020-02-12T20:26:501
 
< 0.1%
2020-02-12T20:14:141
 
< 0.1%
2020-03-06T19:57:141
 
< 0.1%
2020-03-13T14:53:021
 
< 0.1%
2020-03-13T17:37:431
 
< 0.1%
2020-03-06T12:04:591
 
< 0.1%
2020-03-06T20:01:241
 
< 0.1%
Other values (548)548
 
6.7%
(Missing)7622
93.2%

Length

2021-08-21T17:30:19.864830image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-03-18t20:58:131
 
0.2%
2021-01-08t13:54:321
 
0.2%
2020-02-12t20:53:201
 
0.2%
2020-03-20t18:25:221
 
0.2%
2020-06-05t14:59:151
 
0.2%
2020-02-12t20:24:501
 
0.2%
2020-02-13t19:09:011
 
0.2%
2020-02-12t18:20:491
 
0.2%
2020-02-13t17:30:551
 
0.2%
2020-08-19t19:37:461
 
0.2%
Other values (548)548
98.2%

Most occurring characters

ValueCountFrequency (%)
02193
20.7%
21909
18.0%
11155
10.9%
-1116
10.5%
:1116
10.5%
3649
 
6.1%
T558
 
5.3%
4398
 
3.8%
5395
 
3.7%
8342
 
3.2%
Other values (3)771
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7812
73.7%
Dash Punctuation1116
 
10.5%
Other Punctuation1116
 
10.5%
Uppercase Letter558
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02193
28.1%
21909
24.4%
11155
14.8%
3649
 
8.3%
4398
 
5.1%
5395
 
5.1%
8342
 
4.4%
9287
 
3.7%
7285
 
3.6%
6199
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
-1116
100.0%
Uppercase Letter
ValueCountFrequency (%)
T558
100.0%
Other Punctuation
ValueCountFrequency (%)
:1116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10044
94.7%
Latin558
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
02193
21.8%
21909
19.0%
11155
11.5%
-1116
11.1%
:1116
11.1%
3649
 
6.5%
4398
 
4.0%
5395
 
3.9%
8342
 
3.4%
9287
 
2.9%
Other values (2)484
 
4.8%
Latin
ValueCountFrequency (%)
T558
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02193
20.7%
21909
18.0%
11155
10.9%
-1116
10.5%
:1116
10.5%
3649
 
6.1%
T558
 
5.3%
4398
 
3.8%
5395
 
3.7%
8342
 
3.2%
Other values (3)771
 
7.3%

periodoBalanco
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)2.6%
Missing3484
Missing (%)42.6%
Memory size64.0 KiB
2019-12-31T03:00:00
1715 
2019-12-31T00:00:00
1275 
2018-12-31T02:00:00
198 
2020-06-30T03:00:00
 
163
2018-12-31T00:00:00
 
141
Other values (119)
1204 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters89224
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.6%

Sample

1st row2019-09-30T00:00:00
2nd row2019-09-30T03:00:00
3rd row2018-12-31T02:00:00
4th row2019-06-30T03:00:00
5th row2018-12-31T02:00:00

Common Values

ValueCountFrequency (%)
2019-12-31T03:00:001715
21.0%
2019-12-31T00:00:001275
 
15.6%
2018-12-31T02:00:00198
 
2.4%
2020-06-30T03:00:00163
 
2.0%
2018-12-31T00:00:00141
 
1.7%
2020-09-30T03:00:0084
 
1.0%
2020-07-31T03:00:0083
 
1.0%
2019-12-31T06:00:0078
 
1.0%
2019-09-30T03:00:0072
 
0.9%
2020-12-31T03:00:0050
 
0.6%
Other values (114)837
 
10.2%
(Missing)3484
42.6%

Length

2021-08-21T17:30:20.095874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-12-31t03:00:001715
36.5%
2019-12-31t00:00:001275
27.2%
2018-12-31t02:00:00198
 
4.2%
2020-06-30t03:00:00163
 
3.5%
2018-12-31t00:00:00141
 
3.0%
2020-09-30t03:00:0084
 
1.8%
2020-07-31t03:00:0083
 
1.8%
2019-12-31t06:00:0078
 
1.7%
2019-09-30t03:00:0072
 
1.5%
2020-12-31t03:00:0050
 
1.1%
Other values (114)837
17.8%

Most occurring characters

ValueCountFrequency (%)
032308
36.2%
111868
 
13.3%
29489
 
10.6%
-9392
 
10.5%
:9392
 
10.5%
37302
 
8.2%
T4696
 
5.3%
93704
 
4.2%
8438
 
0.5%
6394
 
0.4%
Other values (3)241
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number65744
73.7%
Dash Punctuation9392
 
10.5%
Other Punctuation9392
 
10.5%
Uppercase Letter4696
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032308
49.1%
111868
 
18.1%
29489
 
14.4%
37302
 
11.1%
93704
 
5.6%
8438
 
0.7%
6394
 
0.6%
7154
 
0.2%
549
 
0.1%
438
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-9392
100.0%
Uppercase Letter
ValueCountFrequency (%)
T4696
100.0%
Other Punctuation
ValueCountFrequency (%)
:9392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common84528
94.7%
Latin4696
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
032308
38.2%
111868
 
14.0%
29489
 
11.2%
-9392
 
11.1%
:9392
 
11.1%
37302
 
8.6%
93704
 
4.4%
8438
 
0.5%
6394
 
0.5%
7154
 
0.2%
Other values (2)87
 
0.1%
Latin
ValueCountFrequency (%)
T4696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII89224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032308
36.2%
111868
 
13.3%
29489
 
10.6%
-9392
 
10.5%
:9392
 
10.5%
37302
 
8.2%
T4696
 
5.3%
93704
 
4.2%
8438
 
0.5%
6394
 
0.4%
Other values (3)241
 
0.3%

ativoCirculante
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1791
Distinct (%)38.1%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean44409615.52
Minimum-17
Maximum2.903832 × 1010
Zeros550
Zeros (%)6.7%
Negative1
Negative (%)< 0.1%
Memory size64.0 KiB
2021-08-21T17:30:20.199906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile0
Q1865698.25
median3948479
Q316073062
95-th percentile131141540.2
Maximum2.903832 × 1010
Range2.903832002 × 1010
Interquartile range (IQR)15207363.75

Descriptive statistics

Standard deviation469211882.1
Coefficient of variation (CV)10.56554705
Kurtosis3132.283986
Mean44409615.52
Median Absolute Deviation (MAD)3948478
Skewness51.9597739
Sum2.085475545 × 1011
Variance2.201597903 × 1017
MonotonicityNot monotonic
2021-08-21T17:30:20.315919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0550
 
6.7%
4014767923
 
0.3%
2165299516
 
0.2%
21067790816
 
0.2%
276007414
 
0.2%
30790555914
 
0.2%
1376966014
 
0.2%
1006665912
 
0.1%
29732712
 
0.1%
4253212312
 
0.1%
Other values (1781)4013
49.1%
(Missing)3484
42.6%
ValueCountFrequency (%)
-171
 
< 0.1%
0550
6.7%
12
 
< 0.1%
21971
 
< 0.1%
70634
 
< 0.1%
106362
 
< 0.1%
145512
 
< 0.1%
148831
 
< 0.1%
195841
 
< 0.1%
239721
 
< 0.1%
ValueCountFrequency (%)
2.903832 × 10101
 
< 0.1%
86895130001
 
< 0.1%
24900383282
 
< 0.1%
20617940004
< 0.1%
20617840007
0.1%
14369850001
 
< 0.1%
13347260007
0.1%
12018580008
0.1%
11188860003
 
< 0.1%
9598550001
 
< 0.1%

passivoCirculante
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1787
Distinct (%)38.1%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean33902277.37
Minimum-1134941
Maximum2.750382 × 1010
Zeros587
Zeros (%)7.2%
Negative2
Negative (%)< 0.1%
Memory size64.0 KiB
2021-08-21T17:30:20.435892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1134941
5-th percentile0
Q1181740
median1314989
Q37367297
95-th percentile86406690
Maximum2.750382 × 1010
Range2.750495494 × 1010
Interquartile range (IQR)7185557

Descriptive statistics

Standard deviation496503054
Coefficient of variation (CV)14.64512394
Kurtosis2197.510288
Mean33902277.37
Median Absolute Deviation (MAD)1314989
Skewness43.91583933
Sum1.592050945 × 1011
Variance2.465152827 × 1017
MonotonicityNot monotonic
2021-08-21T17:30:20.546855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0587
 
7.2%
4022259123
 
0.3%
11967191516
 
0.2%
889656316
 
0.2%
74756214
 
0.2%
618107213
 
0.2%
115586612
 
0.1%
331680912
 
0.1%
8873012
 
0.1%
163486200011
 
0.1%
Other values (1777)3980
48.7%
(Missing)3484
42.6%
ValueCountFrequency (%)
-11349411
 
< 0.1%
-3555091
 
< 0.1%
0587
7.2%
11
 
< 0.1%
2091
 
< 0.1%
2704
 
< 0.1%
4852
 
< 0.1%
10003
 
< 0.1%
10043
 
< 0.1%
13501
 
< 0.1%
ValueCountFrequency (%)
2.750382 × 10101
 
< 0.1%
1.305405467 × 10102
 
< 0.1%
27813330001
 
< 0.1%
163486200011
0.1%
10304950007
0.1%
9121040008
0.1%
6777960001
 
< 0.1%
5473809804
 
< 0.1%
5350658534
 
< 0.1%
5233600003
 
< 0.1%

totalAtivo
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1786
Distinct (%)38.0%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean70803376.42
Minimum-17
Maximum5.48235 × 1010
Zeros553
Zeros (%)6.8%
Negative1
Negative (%)< 0.1%
Memory size64.0 KiB
2021-08-21T17:30:20.665858image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile0
Q11024562
median4575580.5
Q318917337
95-th percentile190075283.2
Maximum5.48235 × 1010
Range5.482350002 × 1010
Interquartile range (IQR)17892775

Descriptive statistics

Standard deviation891341861.2
Coefficient of variation (CV)12.58897395
Kurtosis3072.014155
Mean70803376.42
Median Absolute Deviation (MAD)4570152
Skewness51.60937065
Sum3.324926557 × 1011
Variance7.944903136 × 1017
MonotonicityNot monotonic
2021-08-21T17:30:20.784884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0553
 
6.8%
5602827723
 
0.3%
23185118816
 
0.2%
3549093815
 
0.2%
1487809714
 
0.2%
312178414
 
0.2%
4853549512
 
0.1%
51473912
 
0.1%
193737212
 
0.1%
2617129811
 
0.1%
Other values (1776)4014
49.1%
(Missing)3484
42.6%
ValueCountFrequency (%)
-171
 
< 0.1%
0553
6.8%
11
 
< 0.1%
70634
 
< 0.1%
106362
 
< 0.1%
121271
 
< 0.1%
148831
 
< 0.1%
185372
 
< 0.1%
244711
 
< 0.1%
300001
 
< 0.1%
ValueCountFrequency (%)
5.48235 × 10101
 
< 0.1%
1.8083486 × 10101
 
< 0.1%
90146924031
 
< 0.1%
36982159802
 
< 0.1%
363450600011
0.1%
26073510001
 
< 0.1%
21763690001
 
< 0.1%
20764230008
0.1%
17760120007
0.1%
17687420004
 
< 0.1%

totalPatrimonioLiquido
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1757
Distinct (%)37.4%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean28319583.17
Minimum-186719734
Maximum1.292328 × 1010
Zeros585
Zeros (%)7.2%
Negative195
Negative (%)2.4%
Memory size64.0 KiB
2021-08-21T17:30:20.901850image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-186719734
5-th percentile0
Q1232892
median1554333
Q37778616.25
95-th percentile67765135
Maximum1.292328 × 1010
Range1.310999973 × 1010
Interquartile range (IQR)7545724.25

Descriptive statistics

Standard deviation258654427.5
Coefficient of variation (CV)9.133412238
Kurtosis1631.758823
Mean28319583.17
Median Absolute Deviation (MAD)1554333
Skewness36.3371307
Sum1.329887626 × 1011
Variance6.690211289 × 1016
MonotonicityNot monotonic
2021-08-21T17:30:21.028888image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0585
 
7.2%
1513628223
 
0.3%
2320726115
 
0.2%
33101058114
 
0.2%
740237314
 
0.2%
237422114
 
0.2%
632278312
 
0.1%
2493412312
 
0.1%
24436412
 
0.1%
42014612
 
0.1%
Other values (1747)3983
48.7%
(Missing)3484
42.6%
ValueCountFrequency (%)
-1867197342
 
< 0.1%
-1120897802
 
< 0.1%
-1076266301
 
< 0.1%
-578798507
0.1%
-479615771
 
< 0.1%
-195149829
0.1%
-133867202
 
< 0.1%
-106152041
 
< 0.1%
-62130002
 
< 0.1%
-37290081
 
< 0.1%
ValueCountFrequency (%)
1.292328 × 10101
 
< 0.1%
90141741051
 
< 0.1%
26309994122
 
< 0.1%
16132010003
 
< 0.1%
11164930004
 
< 0.1%
11120784443
 
< 0.1%
10036280001
 
< 0.1%
99005900011
0.1%
9106160002
 
< 0.1%
8639170003
 
< 0.1%

endividamento
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1181
Distinct (%)25.1%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean4675972.297
Minimum0
Maximum740631476
Zeros2366
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:21.147850image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3733543
95-th percentile11300000
Maximum740631476
Range740631476
Interquartile range (IQR)733543

Descriptive statistics

Standard deviation37807887.35
Coefficient of variation (CV)8.085567012
Kurtosis261.3130754
Mean4675972.297
Median Absolute Deviation (MAD)0
Skewness15.50390839
Sum2.195836591 × 1010
Variance1.429436346 × 1015
MonotonicityNot monotonic
2021-08-21T17:30:21.262856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02366
28.9%
1577586218
 
0.2%
387760114
 
0.2%
113489012
 
0.1%
100000011
 
0.1%
760332410
 
0.1%
23134419
 
0.1%
46040119
 
0.1%
18474858
 
0.1%
505518
 
0.1%
Other values (1171)2231
27.3%
(Missing)3484
42.6%
ValueCountFrequency (%)
02366
28.9%
11
 
< 0.1%
10003
 
< 0.1%
10361
 
< 0.1%
15002
 
< 0.1%
17802
 
< 0.1%
19752
 
< 0.1%
19791
 
< 0.1%
23481
 
< 0.1%
31602
 
< 0.1%
ValueCountFrequency (%)
7406314761
 
< 0.1%
7210960001
 
< 0.1%
6885000003
< 0.1%
6492140004
< 0.1%
6480000001
 
< 0.1%
6115660003
< 0.1%
3265580003
< 0.1%
2751270002
< 0.1%
1860000001
 
< 0.1%
1850000001
 
< 0.1%

duplicatasAReceber
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1664
Distinct (%)35.4%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean16568990.84
Minimum-22780710
Maximum2.009358 × 1010
Zeros1025
Zeros (%)12.5%
Negative4
Negative (%)< 0.1%
Memory size64.0 KiB
2021-08-21T17:30:21.391864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-22780710
5-th percentile0
Q133007.25
median1074740
Q36370406.75
95-th percentile43565112
Maximum2.009358 × 1010
Range2.011636071 × 1010
Interquartile range (IQR)6337399.5

Descriptive statistics

Standard deviation299050476.8
Coefficient of variation (CV)18.04880452
Kurtosis4330.551804
Mean16568990.84
Median Absolute Deviation (MAD)1074740
Skewness64.64324689
Sum7.780798097 × 1010
Variance8.943118767 × 1016
MonotonicityNot monotonic
2021-08-21T17:30:21.511863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01025
 
12.5%
1479913723
 
0.3%
923955616
 
0.2%
13533030916
 
0.2%
4356511214
 
0.2%
398420812
 
0.1%
1726745212
 
0.1%
673307712
 
0.1%
135872311
 
0.1%
40079900011
 
0.1%
Other values (1654)3544
43.3%
(Missing)3484
42.6%
ValueCountFrequency (%)
-227807102
 
< 0.1%
-13134162
 
< 0.1%
01025
12.5%
11
 
< 0.1%
1462
 
< 0.1%
9241
 
< 0.1%
11431
 
< 0.1%
11782
 
< 0.1%
16131
 
< 0.1%
16532
 
< 0.1%
ValueCountFrequency (%)
2.009358 × 10101
 
< 0.1%
22394550001
 
< 0.1%
19697100001
 
< 0.1%
5393931752
 
< 0.1%
5285420003
 
< 0.1%
4829900003
 
< 0.1%
4761720001
 
< 0.1%
40079900011
0.1%
3416680001
 
< 0.1%
2757710002
 
< 0.1%

estoque
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1672
Distinct (%)35.6%
Missing3484
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean15182847.94
Minimum-263226
Maximum1293428000
Zeros758
Zeros (%)9.3%
Negative3
Negative (%)< 0.1%
Memory size64.0 KiB
2021-08-21T17:30:21.636864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-263226
5-th percentile0
Q1168779.75
median1051455.5
Q35460215
95-th percentile44444561
Maximum1293428000
Range1293691226
Interquartile range (IQR)5291435.25

Descriptive statistics

Standard deviation84087158.61
Coefficient of variation (CV)5.538299462
Kurtosis144.2982673
Mean15182847.94
Median Absolute Deviation (MAD)1051455.5
Skewness11.15196422
Sum7.129865395 × 1010
Variance7.070650242 × 1015
MonotonicityNot monotonic
2021-08-21T17:30:21.763864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0758
 
9.3%
2366458323
 
0.3%
924065716
 
0.2%
4444456116
 
0.2%
407611014
 
0.2%
120695514
 
0.2%
4599065114
 
0.2%
1530537312
 
0.1%
92345212
 
0.1%
27890812
 
0.1%
Other values (1662)3805
46.5%
(Missing)3484
42.6%
ValueCountFrequency (%)
-2632261
 
< 0.1%
-1480981
 
< 0.1%
-1480951
 
< 0.1%
0758
9.3%
11
 
< 0.1%
2911
 
< 0.1%
3902
 
< 0.1%
9422
 
< 0.1%
12501
 
< 0.1%
26532
 
< 0.1%
ValueCountFrequency (%)
129342800011
0.1%
7664660008
0.1%
7005410007
0.1%
5546719512
 
< 0.1%
5150030001
 
< 0.1%
5124730001
 
< 0.1%
5062972024
 
< 0.1%
4688350001
 
< 0.1%
4668350001
 
< 0.1%
4269864787
0.1%

faturamentoBruto
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4281
Distinct (%)52.4%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean55494619.26
Minimum0
Maximum6426115000
Zeros411
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:21.889867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11188238
median3593550
Q315753410
95-th percentile153283885
Maximum6426115000
Range6426115000
Interquartile range (IQR)14565172

Descriptive statistics

Standard deviation334455103.1
Coefficient of variation (CV)6.026802374
Kurtosis209.6705553
Mean55494619.26
Median Absolute Deviation (MAD)3141902
Skewness13.13983139
Sum4.53724007 × 1011
Variance1.11860216 × 1017
MonotonicityNot monotonic
2021-08-21T17:30:22.016864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0411
 
5.0%
10000018
 
0.2%
12077617516
 
0.2%
4427694016
 
0.2%
785149914
 
0.2%
64215114914
 
0.2%
2289815412
 
0.1%
11683079912
 
0.1%
1212369912
 
0.1%
912226211
 
0.1%
Other values (4271)7640
93.4%
ValueCountFrequency (%)
0411
5.0%
13
 
< 0.1%
10001
 
< 0.1%
11232
 
< 0.1%
50143
 
< 0.1%
110754
 
< 0.1%
114341
 
< 0.1%
119462
 
< 0.1%
125002
 
< 0.1%
126001
 
< 0.1%
ValueCountFrequency (%)
642611500011
0.1%
49459260001
 
< 0.1%
44640000001
 
< 0.1%
35085520001
 
< 0.1%
33691730008
0.1%
32224020006
0.1%
29263012014
 
< 0.1%
27700990001
 
< 0.1%
27116010001
 
< 0.1%
24497656001
 
< 0.1%

margemBruta
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1925
Distinct (%)23.5%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16071539.77
Minimum-614872100
Maximum3366842514
Zeros4300
Zeros (%)52.6%
Negative67
Negative (%)0.8%
Memory size64.0 KiB
2021-08-21T17:30:22.145864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-614872100
5-th percentile0
Q10
median0
Q33326431
95-th percentile37393980.75
Maximum3366842514
Range3981714614
Interquartile range (IQR)3326431

Descriptive statistics

Standard deviation116461436.2
Coefficient of variation (CV)7.246439226
Kurtosis307.4601622
Mean16071539.77
Median Absolute Deviation (MAD)0
Skewness15.40189842
Sum1.314009091 × 1011
Variance1.356326612 × 1016
MonotonicityNot monotonic
2021-08-21T17:30:22.269902image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04300
52.6%
3354589116
 
0.2%
1955028416
 
0.2%
296788114
 
0.2%
332352312
 
0.1%
21527337512
 
0.1%
563694512
 
0.1%
361048711
 
0.1%
1821324611
 
0.1%
203257900011
 
0.1%
Other values (1915)3761
46.0%
ValueCountFrequency (%)
-6148721002
 
< 0.1%
-2693832071
 
< 0.1%
-216846281
 
< 0.1%
-102153041
 
< 0.1%
-75405441
 
< 0.1%
-44262532
 
< 0.1%
-41118222
 
< 0.1%
-41118211
 
< 0.1%
-28461718
0.1%
-14691201
 
< 0.1%
ValueCountFrequency (%)
33668425142
 
< 0.1%
203257900011
0.1%
13731430822
 
< 0.1%
10635288884
 
< 0.1%
10390540001
 
< 0.1%
10390000001
 
< 0.1%
10386870004
 
< 0.1%
9981740004
 
< 0.1%
9684480003
 
< 0.1%
8377108793
 
< 0.1%

periodoDemonstrativoEmMeses
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.38270548
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:22.378890image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median12
Q312
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.218857179
Coefficient of variation (CV)0.3100210427
Kurtosis2.19546283
Mean10.38270548
Median Absolute Deviation (MAD)0
Skewness-1.87592319
Sum84889
Variance10.36104154
MonotonicityNot monotonic
2021-08-21T17:30:22.459851image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
126112
74.7%
1457
 
5.6%
6443
 
5.4%
7202
 
2.5%
9188
 
2.3%
11156
 
1.9%
3139
 
1.7%
5137
 
1.7%
10123
 
1.5%
8111
 
1.4%
Other values (2)108
 
1.3%
ValueCountFrequency (%)
1457
5.6%
222
 
0.3%
3139
 
1.7%
486
 
1.1%
5137
 
1.7%
6443
5.4%
7202
2.5%
8111
 
1.4%
9188
2.3%
10123
 
1.5%
ValueCountFrequency (%)
126112
74.7%
11156
 
1.9%
10123
 
1.5%
9188
 
2.3%
8111
 
1.4%
7202
 
2.5%
6443
 
5.4%
5137
 
1.7%
486
 
1.1%
3139
 
1.7%

custos
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1740
Distinct (%)21.3%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28106213.6
Minimum-346633805
Maximum4393536000
Zeros4413
Zeros (%)53.9%
Negative28
Negative (%)0.3%
Memory size64.0 KiB
2021-08-21T17:30:22.561881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-346633805
5-th percentile0
Q10
median0
Q34384631.75
95-th percentile74297132
Maximum4393536000
Range4740169805
Interquartile range (IQR)4384631.75

Descriptive statistics

Standard deviation207165402.2
Coefficient of variation (CV)7.370804376
Kurtosis290.2285942
Mean28106213.6
Median Absolute Deviation (MAD)0
Skewness15.57109389
Sum2.297964024 × 1011
Variance4.291750386 × 1016
MonotonicityNot monotonic
2021-08-21T17:30:22.688863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04413
53.9%
8723028423
 
0.3%
2472665616
 
0.2%
488361814
 
0.2%
8578556113
 
0.2%
42687777413
 
0.2%
1726120912
 
0.1%
880017612
 
0.1%
439353600011
 
0.1%
452090711
 
0.1%
Other values (1730)3638
44.5%
ValueCountFrequency (%)
-3466338053
< 0.1%
-650227015
0.1%
-492677381
 
< 0.1%
-488366471
 
< 0.1%
-433399922
 
< 0.1%
-275926681
 
< 0.1%
-257445971
 
< 0.1%
-94554011
 
< 0.1%
-72315541
 
< 0.1%
-70364741
 
< 0.1%
ValueCountFrequency (%)
439353600011
0.1%
29582350001
 
< 0.1%
23304860004
 
< 0.1%
23301730001
 
< 0.1%
23301190001
 
< 0.1%
19533080002
 
< 0.1%
19397880001
 
< 0.1%
19167620001
 
< 0.1%
18627723134
 
< 0.1%
16008770002
 
< 0.1%

anoFundacao
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct69
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.007579
Minimum1000
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:22.812875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1984
Q12000
median2009
Q32015
95-th percentile2019
Maximum2020
Range1020
Interquartile range (IQR)15

Descriptive statistics

Standard deviation19.46455747
Coefficient of variation (CV)0.009703132564
Kurtosis1743.944328
Mean2006.007579
Median Absolute Deviation (MAD)7
Skewness-34.08083647
Sum16409142
Variance378.8689974
MonotonicityNot monotonic
2021-08-21T17:30:22.935872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017550
 
6.7%
2018474
 
5.8%
2016438
 
5.4%
2013395
 
4.8%
2010392
 
4.8%
2019369
 
4.5%
2011346
 
4.2%
2012339
 
4.1%
2009328
 
4.0%
2008326
 
4.0%
Other values (59)4223
51.6%
ValueCountFrequency (%)
10002
 
< 0.1%
19101
 
< 0.1%
19351
 
< 0.1%
19411
 
< 0.1%
19429
0.1%
19461
 
< 0.1%
19472
 
< 0.1%
19489
0.1%
19516
0.1%
19561
 
< 0.1%
ValueCountFrequency (%)
202052
 
0.6%
2019369
4.5%
2018474
5.8%
2017550
6.7%
2016438
5.4%
2015321
3.9%
2014315
3.9%
2013395
4.8%
2012339
4.1%
2011346
4.2%

intervaloFundacao
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
Acima de 17 anos
2671 
De 0 a 5 anos
2178 
De 6 a 10 anos
1775 
De 11 a 16 anos
1556 

Length

Max length16
Median length15
Mean length14.57701711
Min length13

Characters and Unicode

Total characters119240
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAcima de 17 anos
2nd rowDe 6 a 10 anos
3rd rowDe 6 a 10 anos
4th rowAcima de 17 anos
5th rowDe 6 a 10 anos

Common Values

ValueCountFrequency (%)
Acima de 17 anos2671
32.7%
De 0 a 5 anos2178
26.6%
De 6 a 10 anos1775
21.7%
De 11 a 16 anos1556
19.0%

Length

2021-08-21T17:30:23.134833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-21T17:30:23.201868image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
de8180
21.4%
anos8180
21.4%
a5509
14.4%
acima2671
 
7.0%
172671
 
7.0%
52178
 
5.7%
02178
 
5.7%
61775
 
4.6%
101775
 
4.6%
111556
 
4.1%

Most occurring characters

ValueCountFrequency (%)
30049
25.2%
a16360
13.7%
19114
 
7.6%
e8180
 
6.9%
n8180
 
6.9%
o8180
 
6.9%
s8180
 
6.9%
D5509
 
4.6%
03953
 
3.3%
63331
 
2.8%
Other values (7)18204
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59764
50.1%
Space Separator30049
25.2%
Decimal Number21247
 
17.8%
Uppercase Letter8180
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a16360
27.4%
e8180
13.7%
n8180
13.7%
o8180
13.7%
s8180
13.7%
c2671
 
4.5%
i2671
 
4.5%
m2671
 
4.5%
d2671
 
4.5%
Decimal Number
ValueCountFrequency (%)
19114
42.9%
03953
18.6%
63331
 
15.7%
72671
 
12.6%
52178
 
10.3%
Uppercase Letter
ValueCountFrequency (%)
D5509
67.3%
A2671
32.7%
Space Separator
ValueCountFrequency (%)
30049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin67944
57.0%
Common51296
43.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a16360
24.1%
e8180
12.0%
n8180
12.0%
o8180
12.0%
s8180
12.0%
D5509
 
8.1%
A2671
 
3.9%
c2671
 
3.9%
i2671
 
3.9%
m2671
 
3.9%
Common
ValueCountFrequency (%)
30049
58.6%
19114
 
17.8%
03953
 
7.7%
63331
 
6.5%
72671
 
5.2%
52178
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII119240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30049
25.2%
a16360
13.7%
19114
 
7.6%
e8180
 
6.9%
n8180
 
6.9%
o8180
 
6.9%
s8180
 
6.9%
D5509
 
4.6%
03953
 
3.3%
63331
 
2.8%
Other values (7)18204
15.3%

capitalSocial
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct403
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11144623.17
Minimum0
Maximum4100000000
Zeros126
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:23.295854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9000
Q150000
median100000
Q3500000
95-th percentile6000000
Maximum4100000000
Range4100000000
Interquartile range (IQR)450000

Descriptive statistics

Standard deviation97209768.96
Coefficient of variation (CV)8.722571186
Kurtosis501.4327891
Mean11144623.17
Median Absolute Deviation (MAD)90000
Skewness17.64571427
Sum9.116301755 × 1010
Variance9.449739181 × 1015
MonotonicityNot monotonic
2021-08-21T17:30:23.412842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000001125
 
13.8%
200000441
 
5.4%
10000416
 
5.1%
50000383
 
4.7%
20000304
 
3.7%
30000290
 
3.5%
500000286
 
3.5%
150000269
 
3.3%
1000000261
 
3.2%
300000240
 
2.9%
Other values (393)4165
50.9%
ValueCountFrequency (%)
0126
1.5%
115
 
0.2%
29
 
0.1%
1003
 
< 0.1%
2402
 
< 0.1%
3001
 
< 0.1%
100041
 
0.5%
13001
 
< 0.1%
15007
 
0.1%
19782
 
< 0.1%
ValueCountFrequency (%)
41000000001
 
< 0.1%
25000000001
 
< 0.1%
13422400007
 
0.1%
97995743227
0.3%
9120000001
 
< 0.1%
9000000002
 
< 0.1%
8345102661
 
< 0.1%
5767238581
 
< 0.1%
5620166502
 
< 0.1%
5119031869
 
0.1%

restricoes
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7412 
True
768 
ValueCountFrequency (%)
False7412
90.6%
True768
 
9.4%
2021-08-21T17:30:23.495843image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

empresa_MeEppMei
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
5010 
True
3170 
ValueCountFrequency (%)
False5010
61.2%
True3170
38.8%
2021-08-21T17:30:23.529843image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

scorePontualidade
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct383
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8023519677
Minimum0
Maximum1
Zeros1380
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:23.609842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8928193941
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1071806059

Descriptive statistics

Standard deviation0.3757666139
Coefficient of variation (CV)0.468331392
Kurtosis0.6379332367
Mean0.8023519677
Median Absolute Deviation (MAD)0
Skewness-1.586106589
Sum6563.239095
Variance0.1412005481
MonotonicityNot monotonic
2021-08-21T17:30:23.730840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15135
62.8%
01380
 
16.9%
0.830643615618
 
0.2%
0.998127419315
 
0.2%
0.988885719414
 
0.2%
0.989597025214
 
0.2%
0.986102886113
 
0.2%
0.986135484213
 
0.2%
0.811475858812
 
0.1%
0.994785850712
 
0.1%
Other values (373)1554
 
19.0%
ValueCountFrequency (%)
01380
16.9%
0.00300244716
 
0.1%
0.03869844383
 
< 0.1%
0.06837464656
 
0.1%
0.09551107581
 
< 0.1%
0.10286865422
 
< 0.1%
0.11298890021
 
< 0.1%
0.11457742484
 
< 0.1%
0.11895994921
 
< 0.1%
0.15655140521
 
< 0.1%
ValueCountFrequency (%)
15135
62.8%
0.99999997636
 
0.1%
0.99999986244
 
< 0.1%
0.9999987846
 
0.1%
0.99999307119
 
0.1%
0.99998189617
 
0.1%
0.99997585534
 
< 0.1%
0.99993039458
 
0.1%
0.99991758234
 
< 0.1%
0.99977729336
 
0.1%

limiteEmpresaAnaliseCredito
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1809
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2835607.353
Minimum0
Maximum1974261312
Zeros522
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size64.0 KiB
2021-08-21T17:30:23.853040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17360
median48600
Q3343400.5
95-th percentile8417801
Maximum1974261312
Range1974261312
Interquartile range (IQR)336040.5

Descriptive statistics

Standard deviation26780543.28
Coefficient of variation (CV)9.44437644
Kurtosis3609.907802
Mean2835607.353
Median Absolute Deviation (MAD)46640
Skewness51.10302157
Sum2.319526815 × 1010
Variance7.171974984 × 1014
MonotonicityNot monotonic
2021-08-21T17:30:23.980080image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0522
 
6.4%
720153
 
1.9%
540085
 
1.0%
440079
 
1.0%
1628070
 
0.9%
880069
 
0.8%
1620063
 
0.8%
1080061
 
0.7%
704061
 
0.7%
264058
 
0.7%
Other values (1799)6959
85.1%
ValueCountFrequency (%)
0522
6.4%
720153
 
1.9%
8101
 
< 0.1%
8404
 
< 0.1%
8804
 
< 0.1%
9607
 
0.1%
10003
 
< 0.1%
10802
 
< 0.1%
11001
 
< 0.1%
111017
 
0.2%
ValueCountFrequency (%)
19742613121
 
< 0.1%
2867581449
 
0.1%
2483129601
 
< 0.1%
2436173601
 
< 0.1%
2305231202
 
< 0.1%
1399151523
 
< 0.1%
1381593601
 
< 0.1%
13223315228
0.3%
1284306842
 
< 0.1%
1228083201
 
< 0.1%

dataAprovadoNivelAnalista
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct7011
Distinct (%)100.0%
Missing1169
Missing (%)14.3%
Memory size64.0 KiB
2020-02-07T21:11:17
 
1
2020-05-08T18:10:10
 
1
2020-03-02T21:22:20
 
1
2020-10-30T17:16:20
 
1
2020-02-13T17:29:27
 
1
Other values (7006)
7006 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters133209
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7011 ?
Unique (%)100.0%

Sample

1st row2020-02-03T20:57:33
2nd row2020-02-04T16:40:49
3rd row2020-02-04T16:37:52
4th row2020-02-04T15:06:28
5th row2020-02-04T15:10:46

Common Values

ValueCountFrequency (%)
2020-02-07T21:11:171
 
< 0.1%
2020-05-08T18:10:101
 
< 0.1%
2020-03-02T21:22:201
 
< 0.1%
2020-10-30T17:16:201
 
< 0.1%
2020-02-13T17:29:271
 
< 0.1%
2020-07-23T17:55:081
 
< 0.1%
2020-11-24T20:03:451
 
< 0.1%
2020-09-02T13:08:031
 
< 0.1%
2020-09-10T15:00:101
 
< 0.1%
2020-10-23T13:41:561
 
< 0.1%
Other values (7001)7001
85.6%
(Missing)1169
 
14.3%

Length

2021-08-21T17:30:24.229043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-23t19:46:281
 
< 0.1%
2020-05-28t15:59:341
 
< 0.1%
2021-01-27t15:19:311
 
< 0.1%
2020-12-02t14:18:471
 
< 0.1%
2020-12-07t13:43:261
 
< 0.1%
2020-11-24t20:32:071
 
< 0.1%
2020-12-07t19:38:401
 
< 0.1%
2020-06-25t12:27:121
 
< 0.1%
2020-05-27t15:45:591
 
< 0.1%
2020-09-04t14:57:161
 
< 0.1%
Other values (7001)7001
99.9%

Most occurring characters

ValueCountFrequency (%)
025936
19.5%
224261
18.2%
117113
12.8%
-14022
10.5%
:14022
10.5%
T7011
 
5.3%
35911
 
4.4%
55624
 
4.2%
45382
 
4.0%
93800
 
2.9%
Other values (3)10127
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number98154
73.7%
Dash Punctuation14022
 
10.5%
Other Punctuation14022
 
10.5%
Uppercase Letter7011
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025936
26.4%
224261
24.7%
117113
17.4%
35911
 
6.0%
55624
 
5.7%
45382
 
5.5%
93800
 
3.9%
83667
 
3.7%
73434
 
3.5%
63026
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-14022
100.0%
Uppercase Letter
ValueCountFrequency (%)
T7011
100.0%
Other Punctuation
ValueCountFrequency (%)
:14022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common126198
94.7%
Latin7011
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
025936
20.6%
224261
19.2%
117113
13.6%
-14022
11.1%
:14022
11.1%
35911
 
4.7%
55624
 
4.5%
45382
 
4.3%
93800
 
3.0%
83667
 
2.9%
Other values (2)6460
 
5.1%
Latin
ValueCountFrequency (%)
T7011
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII133209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025936
19.5%
224261
18.2%
117113
12.8%
-14022
10.5%
:14022
10.5%
T7011
 
5.3%
35911
 
4.4%
55624
 
4.2%
45382
 
4.0%
93800
 
2.9%
Other values (3)10127
 
7.6%

Interactions

2021-08-21T17:28:48.281882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.371873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.466876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.577840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.677841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.774838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.883873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:48.994874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.093874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.199875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.300873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.402838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.498862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.596878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.692873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.782840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.878874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:49.974873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.078838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.179842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.279841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.383873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.477873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.566841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.664843image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.758873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.853873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:50.957841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.047841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.139873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.240841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.335838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.431873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.535872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.642839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.740838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.842838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:51.944836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.044877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.139838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.236840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.332839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.425878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.527840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.627841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.734838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.832875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:52.928877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.031844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.132840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.227838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.321838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.412880image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.505872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.608875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.705838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.802839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:53.904874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.007875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.105874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.212875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.318873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.422876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.531840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.637878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.744874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.846842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:54.947841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.045879image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.141839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.240875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.338839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.444874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.542877image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.647847image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.761874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.860871image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:55.955881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.053876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.151872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.253873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.363838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.460876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.559839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.662841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.766874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.870875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:56.978873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.087872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.188873image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.293838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.398840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.505838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.605875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.706875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.805872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:57.909839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.016838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.119839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.233840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.341838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.450840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.568837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.671837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.768842image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.870839image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:58.975840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.075864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.191843image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.294837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.406840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.550032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.674029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.793030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:28:59.929034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.061064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.178029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.303028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.426029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.560030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.680028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.798029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:00.904063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.008057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.121032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.230029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.358028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.472064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.577064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.690063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.794028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.895029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:01.997062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.111028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.226055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.335054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.454063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.573063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.687028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.810029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:02.942053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.070029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.201029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.324030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.450032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.579034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.708032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.843065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:03.963065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.087033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.204028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.320053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.433029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.558064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.698031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.857029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:04.992032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.116030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.226064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.345032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.457064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.564033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.699032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.831033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:05.961028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.084030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.198030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.323030image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.454029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.604031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.757065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:06.908032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:07.048056image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:07.193036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:07.341036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:07.497031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:07.777035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:07.968033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-08-21T17:29:08.154031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-08-21T17:30:01.989261image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-08-21T17:30:12.938262image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-08-21T17:30:24.358079image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-21T17:30:24.658082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-21T17:30:24.954974image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-21T17:30:25.274926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-21T17:30:25.555961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-21T17:30:13.243261image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-21T17:30:14.361301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-21T17:30:14.756298image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-21T17:30:15.048304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexnumero_solicitacaorazaoSocialnomeFantasiacnpjSemTracomaiorAtrasomargemBrutaAcumuladapercentualProtestosprimeiraCompraprazoMedioRecebimentoVendastitulosEmAbertovalorSolicitadostatusdefinicaoRiscodiferencaPercentualRiscopercentualRiscodashboardCorrelacaovalorAprovadodataAprovadoEmComiteperiodoBalancoativoCirculantepassivoCirculantetotalAtivototalPatrimonioLiquidoendividamentoduplicatasAReceberestoquefaturamentoBrutomargemBrutaperiodoDemonstrativoEmMesescustosanoFundacaointervaloFundacaocapitalSocialrestricoesempresa_MeEppMeiscorePontualidadelimiteEmpresaAnaliseCreditodataAprovadoNivelAnalista
001James Richardson-PatelAlexandra WilliamsKEBE1760949222084300.2524480.02015-12-10T00:00:0000.0050000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.7169810.2830190.000000e+0050000.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1766880.00.012.00.02003.0Acima de 17 anos90000.0FalseTrue1.043200.02020-02-03T20:57:33
123Joanna HudsonDr. David ReesGCVQ2853161426129340.6247770.02019-11-27T00:00:0000.0020000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.7169810.2830190.000000e+0020000.0NaNNaNNaNNaNNaNNaNNaNNaNNaN2814940.00.07.00.02014.0De 6 a 10 anos20000.0FalseTrue1.04320.02020-02-04T16:40:49
234Gordon Jones-HopkinsSara Reid-RobsonKJND32266018316396200.000000NaN2017-02-13T17:20:2700.0025000.0AprovadoAnalistaDe 51 a 80 % - Alto0.3962260.6037744.858111e-0115000.0NaNNaNNaNNaNNaNNaNNaNNaNNaN1285274.00.012.00.02013.0De 6 a 10 anos30000.0FalseTrue0.05920.02020-02-04T16:37:52
345Nigel LeeDr. Stanley DuncanCGQN15826802440348200.4540880.02010-07-13T00:00:00201486.9550000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.8301890.1698110.000000e+0050000.0NaN2019-09-30T00:00:0014758917.012149031.025793410.014544378.03039112.011797928.03047791.040779757.081459809.09.040680051.02002.0Acima de 17 anos75000.0FalseFalse1.089000.02020-02-04T15:06:28
456Liam JacksonGerard GordonKAYS5323202730692500.000000NaN2019-06-28T10:25:5700.0025000.0AprovadoAnalistaDe 31 a 50 % - Médio0.6226420.3773580.000000e+0020000.0NaNNaNNaNNaNNaNNaNNaNNaNNaN918476.00.012.00.02011.0De 6 a 10 anos15000.0FalseTrue0.038400.02020-02-04T15:10:46
567Alexander Baker-WellsDr. Caroline AliPYOL43118620147076160.000000NaN2011-03-02T11:27:1300.00100000.0AprovadoAnalistaDe 31 a 50 % - Médio0.5849060.415094-9.975171e-0180000.0NaNNaNNaNNaNNaNNaNNaNNaNNaN239016696.0-7540544.012.0156394112.01982.0Acima de 17 anos30515674.0TrueFalse0.018132588.02020-02-05T14:31:22
678Jean BerryGregory GouldNLUX9349621617646340.3501750.02007-07-02T00:00:00102208980.00300000.0AprovadoAnalistaDe 0 a 10 % - Muito Baixo0.9433960.0566040.000000e+00300000.0NaN2019-09-30T03:00:0017801610.02538069.019415777.07519758.00.03742963.011902384.011958227.09357949.09.00.02000.0Acima de 17 anos120000.0FalseFalse1.0768233.02020-02-05T20:05:40
789Elliot StephensonAdrian GrahamLOVL82962402474134430.2458180.02006-07-10T00:00:00276432.00400000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.8867920.1132080.000000e+00400000.0NaN2018-12-31T02:00:0036827690.013843198.039455945.025612746.00.015980275.011420388.072479551.06233236.012.0-49267738.02000.0Acima de 17 anos1000000.0FalseFalse1.06910219.02020-02-05T20:07:07
81011Katie LawsonPatricia ReynoldsSVXA1623029713511970.000000NaN2012-02-24T16:01:2600.001000000.0AprovadoAnalistaDe 31 a 50 % - Médio0.6037740.396226-5.091516e-01800000.0NaN2019-06-30T03:00:002127907.0704245.03609498.02538787.0366465.0973971.0942096.04575646.0296634.06.0-3299332.02011.0De 6 a 10 anos20000.0FalseTrue0.081000.02020-02-05T15:15:10
91112Ellie TurnerMohamed WelchELQK41348591516215260.000000NaN2017-12-20T17:03:3200.00200000.0AprovadoComiteDe 31 a 50 % - Médio0.5849060.415094-3.000000e-1550000.02020-02-05T19:14:372018-12-31T02:00:005369982.06118711.05595689.0-720844.01715000.0115000.01693990.013871246.0-772398.012.0-9455401.02009.0De 11 a 16 anos600000.0FalseFalse0.0183509.0NaN

Last rows

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817289559028Andrew Noble-CollinsCarl MartinMHSR6669780268264500.3878600.02020-12-21T00:00:003920907.0050000.0AprovadoAnalistaDe 0 a 10 % - Muito Baixo0.9107140.0892860.00000050000.0NaN2019-12-31T00:00:005801231.04585834.05910905.0661773.0948266.02832100.02093270.020444406.03235672.012.017208734.01985.0Acima de 17 anos300000.0FalseFalse1.000000720.02021-02-25T20:06:47
817389569029Jeffrey PatelSusan SmithKBOS63147302240824180.000000NaN2017-02-13T16:41:0300.0050000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.7857140.214286-0.07637545000.0NaNNaNNaNNaNNaNNaNNaNNaNNaN3410070.00.012.00.02012.0De 6 a 10 anos200000.0FalseFalse0.000000185870.02021-02-25T20:07:50
817489579030Mr. Gerald WilsonKatie BarlowISPY8139376011706140.1944290.02019-05-16T00:00:0000.00200000.0AprovadoAnalistaDe 31 a 50 % - Médio0.6250000.3750000.00000015000.0NaN2019-12-31T03:00:002230968.013885.02336218.02322332.00.00.038112.01366363.01153580.012.0212783.02017.0De 0 a 5 anos100000.0FalseTrue1.00000055500.02021-02-25T20:13:33
817589589031Joel AllanGavin HoldenXIIJ6667004014488440.5367390.02013-02-07T00:00:00286804.001500000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.7500000.250000-0.8660251500000.0NaN2019-12-31T03:00:0020070708.018164050.020290799.01893943.074793.011285664.05718842.0133280394.044612644.012.088667750.02010.0De 11 a 16 anos833345.0FalseFalse0.9990057200000.02021-02-25T20:33:36
817689599032Douglas TaylorDale LoweJXCH36268697453955180.3745730.02014-02-21T00:00:004490290.90250000.0ReprovadoAnalistaDe 11 a 30 % - Baixo0.8571430.142857-0.162833NaNNaN2020-12-31T03:00:004943835.04025065.05633038.0905806.01257000.03471368.01316604.04887005.02322234.012.02564771.02009.0De 11 a 16 anos30000.0FalseFalse0.83064460000.0NaN
817789609033Jamie CoxKirsty JonesIPFS7063589407380800.4622500.02020-07-31T00:00:0000.0020000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.7321430.2678570.00000020000.0NaN2019-12-31T00:00:000.00.00.00.00.00.00.05392166.0246578.012.05145588.02018.0De 0 a 5 anos110000.0FalseTrue1.0000003240.02021-02-25T20:40:58
817889619034Sara FosterDean PriceIFOK15843566708440640.4266520.02006-07-07T00:00:00641276462.661800000.0AprovadoAnalistaDe 11 a 30 % - Baixo0.8571430.142857-0.0781921800000.0NaN2020-12-29T21:00:0038305921.015063464.040876769.025603650.046198.015784605.015243870.0119702196.038587967.012.081114229.01995.0Acima de 17 anos2000000.0FalseFalse0.99993010833160.02021-02-25T20:10:15
817989639036June LaneMiss Sharon PetersVKMN77216991472135180.4901660.02017-07-18T00:00:004733269.2650000.0AguardandoAprovacaoDe 11 a 30 % - Baixo0.8750000.1250000.999533NaNNaN2019-12-31T00:00:003413036.01115529.03850888.02735358.00.02473428.0892027.04699829.02548248.012.02151581.02013.0De 6 a 10 anos280000.0FalseFalse1.00000052000.0NaN